Baseline Climate Grid Resolution and Climate Time Step Impacts on Desert Vegetation Habitat Models

2020 ◽  
Vol 11 (4) ◽  
pp. 79-100
Author(s):  
Ross J. Guida ◽  
Scott R. Abella
Keyword(s):  
Spatial Resolution ◽  
Mojave Desert ◽  
Environmental Modeling ◽  
Grid Resolution ◽  
Major Influence ◽  
Desert Plant ◽  
Climate Data ◽  
Distribution Models ◽  
Time Step ◽  
Desert Vegetation

While it is often the assumption in environmental modeling that finer-resolution modeling is preferred, especially if computation times are not prohibitive, few studies have assessed how climate grid resolution influences the Maxent-predicted habitat of desert vegetation species. Further, drought events can occur over longer or shorter terms with drought length potentially influencing species' habitat distributions. This study uses four higher-elevation Mojave Desert plant species experiencing known habitat contractions corresponding with climatic changes to assess how sensitive Maxent species distribution models are to using 5- and 10-year averaged climate data, as well as 800-m and 4-km resampled gridded climate data. Results show there are spatial differences in models despite relatively consistent clustering of three of the species' recorded field locations, whereas predicting habitat for the more broadly ranging species results in less certainty across all models. Overall, models were more sensitive to the spatial resolution of the climate data than to the climate time step used. When considering geographic areas with high relief, such as the Newberry Mountains in southern Nevada constituting the study area, the spatial resolution of climate data has a major influence on modeled habitat. As more fine-resolution climate data become available, researchers may need to establish more plots for field collection to assess specific microclimatic effects on vegetation.

scholarly journals PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mojtaba Sadeghi ◽  
Phu Nguyen ◽  
Matin Rahnamay Naeini ◽  
Kuolin Hsu ◽  
Dan Braithwaite ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Heavy Precipitation ◽  
Climate Data ◽  
Spatiotemporal Resolution ◽  
Data Record ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Global Precipitation ◽  
Climate Data Record

AbstractAccurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.

Alternative climate data sources for distributed hydrological modelling on a daily time step

2010 ◽  
Vol 25 (10) ◽  
pp. 1542-1557 ◽  
Author(s):  
Ashraf El-Sadek ◽  
Max Bleiweiss ◽  
Manoj Shukla ◽  
Steve Guldan ◽  
Alexander Fernald
Keyword(s):  
Hydrological Modelling ◽  
Data Sources ◽  
Climate Data ◽  
Time Step ◽  
Daily Time Step ◽  
Distributed Hydrological Modelling ◽  
Daily Time

scholarly journals megaSDM: integrating dispersal and time‐step analyses into species distribution models

Ecography ◽  
2021 ◽  
Author(s):  
Benjamin R. Shipley ◽  
Renee Bach ◽  
Younje Do ◽  
Heather Strathearn ◽  
Jenny L. McGuire ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Distribution Models ◽  
Time Step

scholarly journals Version 2 of the EUMETSAT OSI SAF and ESA CCI Sea Ice Concentration Climate Data Records

2018 ◽  
Author(s):  
Thomas Lavergne ◽  
Atle Macdonald Sørensen ◽  
Stefan Kern ◽  
Rasmus Tonboe ◽  
Dirk Notz ◽  
...  
Keyword(s):  
Sea Ice ◽  
Satellite Data ◽  
Grid Point ◽  
Ice Cover ◽  
Quantitative Information ◽  
Horizontal Resolution ◽  
Climate Data ◽  
Time Step ◽  
Sea Ice Concentration ◽  
Ice Concentration

Abstract. We introduce the OSI-450, the SICCI-25km and the SICCI-50km climate data records of gridded global sea-ice concentration. These three records are derived from passive microwave satellite data and offer three distinct advantages compared to existing records: First, all three records provide quantitative information on uncertainty and possibly applied filtering at every grid point and every time step. Second, they are based on dynamic tie points, which capture the time evolution of surface characteristics of the ice cover and accommodate potential calibration differences between satellite missions. Third, they are produced in the context of sustained services offering committed extension, documentation, traceability, and user support. The three records differ in the underlying satellite data (SMMR & SSM/I & SSMIS or AMSR-E & AMSR2), in the imaging frequency channels (37 GHz and either 6 GHz or 19 GHz), in their horizontal resolution (25 km or 50 km) and in the time period they cover. We introduce the underlying algorithms and provide an initial evaluation. We find that all three records compare well with independent estimates of sea-ice concentration both in regions with very high sea-ice concentration and in regions with very low sea-ice concentration. We hence trust that these records will prove helpful for a better understanding of the evolution of the Earth's sea-ice cover.

scholarly journals Estimates of fire emissions from an active deforestation region in the southern Amazon based on satellite data and biogeochemical modelling

2009 ◽  
Vol 6 (2) ◽  
pp. 235-249 ◽  
Author(s):  
G. R. van der Werf ◽  
D. C. Morton ◽  
R. S. DeFries ◽  
L. Giglio ◽  
J. T. Randerson ◽  
...  
Keyword(s):  
Interannual Variability ◽  
Spatial Resolution ◽  
Biogeochemical Model ◽  
Land Uses ◽  
Time Step ◽  
Detailed Model ◽  
Mato Grosso ◽  
Fire Emissions ◽  
Deforestation Rates ◽  
In Fire

Abstract. Tropical deforestation contributes to the build-up of atmospheric carbon dioxide in the atmosphere. Within the deforestation process, fire is frequently used to eliminate biomass in preparation for agricultural use. Quantifying these deforestation-induced fire emissions represents a challenge, and current estimates are only available at coarse spatial resolution with large uncertainty. Here we developed a biogeochemical model using remote sensing observations of plant productivity, fire activity, and deforestation rates to estimate emissions for the Brazilian state of Mato Grosso during 2001–2005. Our model of DEforestation CArbon Fluxes (DECAF) runs at 250-m spatial resolution with a monthly time step to capture spatial and temporal heterogeneity in fire dynamics in our study area within the ''arc of deforestation'', the southern and eastern fringe of the Amazon tropical forest where agricultural expansion is most concentrated. Fire emissions estimates from our modelling framework were on average 90 Tg C year−1, mostly stemming from fires associated with deforestation (74%) with smaller contributions from fires from conversions of Cerrado or pastures to cropland (19%) and pasture fires (7%). In terms of carbon dynamics, about 80% of the aboveground living biomass and litter was combusted when forests were converted to pasture, and 89% when converted to cropland because of the highly mechanized nature of the deforestation process in Mato Grosso. The trajectory of land use change from forest to other land uses often takes more than one year, and part of the biomass that was not burned in the dry season following deforestation burned in consecutive years. This led to a partial decoupling of annual deforestation rates and fire emissions, and lowered interannual variability in fire emissions. Interannual variability in the region was somewhat dampened as well because annual emissions from fires following deforestation and from maintenance fires did not covary, although the effect was small due to the minor contribution of maintenance fires. Our results demonstrate how the DECAF model can be used to model deforestation fire emissions at relatively high spatial and temporal resolutions. Detailed model output is suitable for policy applications concerned with annual emissions estimates distributed among post-clearing land uses and science applications in combination with atmospheric emissions modelling to provide constrained global deforestation fire emissions estimates. DECAF currently estimates emissions from fire; future efforts can incorporate other aspects of net carbon emissions from deforestation including soil respiration and regrowth.

Remote Sensing of African Rainfall

2020 ◽  
Author(s):  
Tufa Dinku
Keyword(s):  
Climate Variability ◽  
Spatial Resolution ◽  
Rural Areas ◽  
Well Being ◽  
Early Warning Systems ◽  
Climate Data ◽  
Climate Variability And Change ◽  
Climate Services ◽  
Spatial Coverage ◽  
Satellite Rainfall

Climate data support a suite of scientific and socioeconomic activities that can reinforce development gains and improve the lives of those most vulnerable to climate variability and change. Historical and current weather and climate observations are essential for many activities, including operational meteorology, identifying extreme events and assessing associated risks, developing climate-informed early warning systems, planning, and research. Rainfall is the most widely available and used climate variable. Thus, measurement of rainfall is crucial to society’s well-being. In general, measurements from ground meteorological stations managed by National Meteorological Agencies are the principal sources of rainfall data. The main strength of the station observations is that they are assumed to give the “true” measurements of rainfall. However, the distribution of the meteorological observation network over Africa is significantly inadequate, with declining numbers of stations and poor data quality. This problem is compounded by the fact that the distribution of existing stations is uneven, with most weather stations located in cities and towns along major roads. As a result, coverage tends to be worse in rural areas, where livelihoods may be most vulnerable to climate variability and change. This has resulted in critical gaps in the provision of climate services where it is needed the most. Space-based measurements from satellites are being used as a complement to or in place of ground observations. Satellite-derived precipitation estimates offer good spatial coverage and improved temporal and spatial resolution, as well as near-real-time availability. Moreover, a range of satellite rainfall products are freely available from many sources, and a couple of these products are available only for Africa. However, satellite rainfall products also suffer from many shortcomings that include accuracy, particularly at higher temporal resolutions; coarse spatial resolution; short time series; and temporal inhomogeneity due to varying inputs. This limits the use of the use these products for certain applications. Understanding satellite rainfall estimation errors is critical for deciding which products might be used for specific applications and requires rigorous evaluation of these products using ground observations. The challenge in Africa is lack of availability, accessibility, and quality of rain-gauge observations that could be used for this purpose. Despite these challenges, there have been some validation efforts over different parts of the continent. However, different and inconsistent approaches of validation have created challenges to using these evaluation results. A comprehensive validation of the main operational satellite products at a continental level is needed to overcome these challenges and make the best use of satellite rainfall products in different applications.

scholarly journals Influence analysis of climate data time-step on the accuracy of HAM simulation

2018 ◽  
Vol 196 ◽  
pp. 02029 ◽  
Author(s):  
Peter Juras ◽  
Daniela Jurasova
Keyword(s):  
Moisture Transport ◽  
Energy Demand ◽  
Simulation Software ◽  
Weather Station ◽  
University Campus ◽  
Climate Data ◽  
Time Step ◽  
Building Simulations ◽  
The University ◽  
Steady Heat

Scientific research in the area of building simulations has a great potential and it is continuously developing and advancing. Computer simulations are helpful in many areas of Civil Engineering, such as energy demand, moisture transport, thermal comfort, ventilation etc. Climate data measured by experimental weather station are analyzed in this article. Weather station is located within the University campus and data recorded with a short are used in a non-steady heat-air-moisture simulation. Climate parameters differences caused by the various averaging periods are shown. This differences are also analyzed in term of outdoor surface temperatures calculated with WUFI Pro simulation software.

scholarly journals Estimation of tropical cyclone wind hazards in coastal regions of China

2020 ◽  
Vol 20 (6) ◽  
pp. 1617-1637 ◽  
Author(s):  
Genshen Fang ◽  
Lin Zhao ◽  
Shuyang Cao ◽  
Ledong Zhu ◽  
Yaojun Ge
Keyword(s):  
Wind Field ◽  
Radial Pressure ◽  
Disaster Mitigation ◽  
Japan Meteorological Agency ◽  
Coastal Regions ◽  
Distribution Models ◽  
Time Step ◽  
Translation Speed ◽  
Wind Hazards ◽  
Hazard Curves

Abstract. Coastal regions of China feature high population densities as well as wind-sensitive structures and are therefore vulnerable to tropical cyclones (TCs) with approximately six to eight landfalls annually. This study predicts TC wind hazard curves in terms of design wind speed versus return periods for major coastal cities of China to facilitate TC-wind-resistant design and disaster mitigation as well as insurance-related risk assessment. The 10 min wind information provided by the Japan Meteorological Agency (JMA) from 1977 to 2015 is employed to rebuild TC wind field parameters (radius of maximum winds Rmax,s and shape parameter of radial pressure profile Bs) at surface level using a height-resolving boundary layer model. These parameters will be documented to develop an improved JMA dataset. The probabilistic behaviors of historical tracks and wind field parameters at the first time step within a 500 km radius subregion centered at a site of interest are examined to determine preferable probability distribution models before stochastically generating correlated genesis parameters utilizing the Cholesky decomposition method. Recursive models are applied for translation speed, Rmax,s and Bs during the TC track and wind field simulations. Site-specific TC wind hazards are studied using 10 000-year Monte Carlo simulations and compared with code suggestions as well as other studies. The resulting estimated wind speeds for northern cities (Ningbo and Wenzhou) under a TC climate are higher than code recommendations, while those for southern cities (Zhanjiang and Haikou) are lower. Other cities show a satisfactory agreement with code provisions at the height of 10 m. Some potential reasons for these findings are discussed to emphasize the importance of independently developing hazard curves of TC winds.

scholarly journals Temporal and Spatial Interpolation Errors of High-Resolution Modeled Atmospheric Fields

2016 ◽  
Vol 33 (2) ◽  
pp. 303-311 ◽  
Author(s):  
N. C. Privé ◽  
R. M. Errico
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
General Circulation ◽  
Temporal Frequency ◽  
General Circulation Models ◽  
Specific Humidity ◽  
Tropospheric Temperature ◽  
Model Output ◽  
Time Step ◽  
Optimal Output

AbstractGeneral circulation models can now be run at very high spatial resolutions to capture finescale features, but saving the full-spatial-resolution output at every model time step is usually not practical because of storage limitations. To reduce storage requirements, the model output may be produced at reduced temporal and/or spatial resolutions. When this reduced-resolution output is then used in situations where spatiotemporal interpolation is required, such as the generation of synthetic observations for observing system simulation experiments, interpolation errors can significantly affect the quality and usefulness of the reduced-resolution model output. Although it is common in practice to record model output at the highest possible spatial resolution with relatively infrequent temporal output, this may not be the best option to minimize interpolation errors. In this study, two examples using a high-resolution global run of the Goddard Earth Observing System Model, version 5 (GEOS-5), are presented to illustrate cases in which the optimal output dataset configurations for interpolation have high temporal frequency but reduced spatial resolutions. Interpolation errors of tropospheric temperature, specific humidity, and wind fields are investigated. The relationship between spatial and temporal output resolutions and interpolation errors is also characterized for the example model.

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